Unveiling the role of KRAS in Chinese colorectal cancer patients: a positive influence on tumor mutational burden

Introduction

Globally, colorectal cancer (CRC) is the third most increasingly common cancer, and it is associated with high mortality and morbidity rates in individuals aged >65 years (1). Extensive research has been performed on the subject, and the pathogenesis of CRC is known to be attributable to modifiable risk factors including diet, lifestyle, and genetic predisposition (2). In addition, it is noteworthy that China reports more than 600,000 new cases and 280,000 deaths cases of CRC in 2020, accounting for approximately 30% of the global deaths (3). The fact that colon epithelial cell growth achieves malignancy through the progressive accumulation of variations in genetic and epigenetic information is well known (4,5). Several molecular and genetic studies have attempted to confirm whether the involvement of genetic alternations affects CRC progression and development; mutations such as TP53, KRAS, and APC are the most widely reported (6-8).

Currently, the field of cancer research involving comprehensive analysis to elucidate mutations of key driver genes or genetic biomarkers related to immunotherapy has been progressively garnering increasing attention (9-11). As the most prevalent driver oncogene in CRC, KRAS mutation is noted in approximately 40–50% of CRC patients (12). KRAS mutations result in the activation of downstream signaling pathways, which facilitate uncontrolled cellular proliferation and confer resistance to specific targeted therapies, such as anti-epidermal growth factor receptor (EGFR) treatments (13). Additionally, the KRAS gene is known to have common mutant subtypes including G12C, G12V, G12A, and G13D; whereas the prognostic value in various tumor types remains incomplete and unclear (14,15). This may raise concerns regarding the actual prognostic value, which can be evaluated through the examination of additional mutant subtypes. Some studies have indicated a potential association between certain subtypes of KRAS mutations and increased levels of tumor mutational burden (TMB) in certain cancers, such as non-small cell lung cancer (NSCLC). There is evidence that KRAS G12C mutation has shown a high TMB and elevated expression of programmed cell death ligand 1 (PD-L1), which contributes to a higher likelihood of responding to immune checkpoint inhibitors (ICIs) (16). Further studies have proposed that KRAS G12D presents immunosuppressive features through driving immune checkpoint blockade and reducing response to ICIs in lung adenocarcinoma. This study reveals that the expression levels of TMB (median, G12V, 4.68 vs. G12D, 2.38) and PD-L1 are significantly elevated in the KRAS G12V subtype when compared to the KRAS G12D subtype (P<0.01). These findings imply a greater probability for KRAS G12V to derive benefits from immunotherapy in comparison to KRAS G12D (17-19). In fact, the presence of KRAS mutations may affect the immune response within the tumor microenvironment and potentially impact the efficacy of ICIs. KRAS mutation is associated with specific molecular subtypes of CRC, such as microsatellite stable (MSS) tumors, which generally have a lower TMB compared to microsatellite instability-high (MSI-H) tumors (20,21). It has been widely accepted that patients with CRC who have MSI-H or deficient DNA mismatch repair (dMMR) improves clinical outcomes when treated with ICIs (22).

Accordingly, we performed a comprehensive genomic landscape analysis on samples from 94 patients diagnosed with CRC using next-generation sequencing (NGS) technology. In this study, we conducted an analysis of the mutational signature associated with various driver genes and investigated the expression level of TMB in relation to co-occurring genomic alterations and single gene mutation. In addition, we analyzed the impact of different KRAS-mutant subtypes on TMB and explored the potential of KRAS mutations as biomarkers to indirectly predict the efficacy of ICIs by impacting TMB. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-600/rc).

Methods

Patients and samples

A total of 103 patients with metastatic CRC (mCRC) were recruited between June 2019 and January 2020 at the Sun Yat-sen University Cancer Center. However, 9 of these patients were excluded from all testing procedures due to the fact that less than 20% of their tumors were present on formalin-fixed paraffin-embedded (FFPE) sections. Consequently, the data considered for further analysis were derived from the sequencing results of the remaining 94 patients. The main inclusion criteria were as follows: adults aged ≥18 years; complete clinicopathological information; and confirmed diagnosis of CRC using imaging techniques, including endoscopy, ultrasound, magnetic resonance imaging, and computed tomography, among others. Clinical response and tumor burden were evaluated according to the Response Evaluation Criteria in Solid Tumors, V.1.1 (23). All data were publicly available and patients with data deficiency were excluded. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by Sun Yat-sen University Cancer Center Ethics Committee (B2019-031-01, Guangdong, China) and informed consent was provided by all individual participants.

DNA extraction, library construction, and targeted sequencing

All tumor samples were reviewed by in-house pathologists and only samples with 30% or more of tumor cellularity were accepted for analysis. DNA from fresh tissue samples and FFPE tumor tissue samples were extracted using the EasyPure^® Genomic DNA Kit (Beijing TransGen Biotech, Beijing, China) and QIAamp DNA FFPE Kit (QIAGEN, Valencia, CA, USA), respectively. DNA fragments and circulating tumor DNA (ctDNA) were used to construct a library by using the KAPA Library Preparation kit (Kapa Biosystems, Inc., Wilmington, MA, USA). Hybridization-based target enrichment was performed using a HaploX pan-cancer panels, a validated customized panel targeted 680 cancer-related genes (table available at https://cdn.amegroups.cn/static/public/TCR-24-600-1.docx). Library fragment size and DNA sequencing were performed by using the Agilent 4200 TapeStation system (Agilent Technologies, Santa Clara, CA, USA) and Illumina Novaseq 6000 system (Illumina, San Diego, CA, USA). Sequencing data were filtered by fastp version 0.18.0 (https://github.com/OpenGene/fastp) and aligned to the hg19 genome (GRch37) (24). Data quality control was performed using the following criteria: the ratio of remaining data filtered by fastq in raw data was ≥85%; the proportion of Q30 bases was ≥85%; the ratio of reads on the reference genome was ≥85%; target region coverage of ≥98%; and the mean tissue sequencing depth of ≥500×.

Damaging variation, calculation of TMB, and definition of driver genes

Damaging or neutral variants were defined and predicted according to the records and rules of the Catalogue Of Somatic Mutations In Cancer (COSMIC) database (25). TMB calculation was performed by dividing the total number of tissue non-synonymous single nucleotide polymorphism (SNP) and insertion/deletion (indel) variations (allele frequency, ≥5%) by the size of the coding region of the 680 panels (26-28).

The definition of MSS and MSI

Based on the recommendations of the National Cancer Institute (NCI), 5 microsatellite sites—including NR21, NR24, NR27, BAT25, and BAT26 were used to determine microsatellite instability (MSI) (29). The tumors were classified as follows: 1 of the 5 sites is altered, low microsatellite instability (MSI-L); 2 or more of the 5 sites are altered, MSI-H; none of the 5 microsatellite sites is altered, MSS (30).

Statistical and data analyses

Statistical analysis was performed and figures were plotted using the GraphPad Prism 5.0 software (GraphPad Software, Inc., La Jolla, CA, USA). Student’s t-test was performed when comparing 2 groups, and analysis of variance (ANOVA) and post hoc tests were performed when comparing 3 or more groups. Mutation spectrum figures were designed using the R software (https://www.r-project.org/) (*, P<0.05; **, P<0.01; ***, P<0.001).

Results

Patient clinical characteristics

A total of 94 patients, with ages ranging from 30 to 88 years and a median age of 59 years, were enrolled in this study. Detailed clinical characteristics pertaining to CRC, specifically colon cancer, rectal cancer, and mCRC, were recorded for each patient (Table 1). In the population of participants, the proportion of males was 58%, whereas 44.66% of patients presented a family history. Patients were diagnosed with CRC along with varying degrees of lung, hepatic, and renal metastases. Among these patients, 53.40% had colon cancer, whereas 27.18% had rectal cancer. Approximately 7.45% of patients exhibited tumors with MSI-L status, whereas 4.26% of tumors were determined to have MSI-H status.

Somatic mutational signature of driver genes

In this study, a total of 311 genes exhibiting frequent genomic alterations were analyzed through targeted NGS. Overall, pathogenic mutations were observed across all somatic chromosomes in which 3 important mutation types were identified: synonymous (silent mutation), non-synonymous (nonsense, missense, and frameshift), and splice-site mutations. Furthermore, 257 non-silent and 10 synonymous mutations were determined, respectively. The top mutated genes were TP53 (81.91%, n=77), APC (71.28%, n=67), KRAS (43.62%, n=41), SYNE1 (22.34%, n=21), LRP1B (18.09%, n=17), PIK3CA (17.02%, n=16), SMAD4 (12.77%, n=12), BRAF (9.57%, n=9), and ATM (6.38%, n=6) in 94 samples (Figure 1 & table available at https://cdn.amegroups.cn/static/public/TCR-24-600-2.docx). The NRAS (1.06%, n=1) mutation as a key prognostic hotspot biomarker was also detected. The majority of the variants were classified as frequent single nucleotide variants (SNVs) (98.50%, 263/267), whereas a small proportion of indel mutations were observed (1.50%, 4/267). Notably, a significant association was observed between MSI-H and TMB-high (TMB-H) in the majority of analyzed tumors.

Figure 1 Genomic landscape and mutational hotspots in CRC patients. Mutational landscape of patients with CRC for all cases involved in this study. All obtained genes were aligned according to mutational frequency, in which SNV, indel, TMB, and MSI status were also presented. Mutational frequency was positively correlated with color. TMB, tumor mutational burden; MSI, microsatellite instability; MSI-L, low microsatellite instability; MSS, microsatellite stable; MSI-H, high microsatellite instability; TMB, tumor mutational burden; TMB-L, tumor mutational burden-low; TMB-H, tumor mutational burden-high; CRC, colorectal cancer; SNV, single nucleotide variant; indel, insertion/deletion.

In order to acquire a more systematic perspective of KRAS mutation, this study focused on analyzing the frequency signature of KRAS, TP53, BRAF, PIK3CA, and APC. The co-mutation of KRAS/APC (31.92%, n=30) exhibited the highest frequency of occurrence, followed by KRAS/BRAF (29.79%, n=28), KRAS/TP53 (28.72%, n=27), and KRAS/PIK3CA (27.66%, n=26) (Figure 2A-2D). Mutated subtypes including several common allosteric sites of KRAS such as G12D, G12C, G12V, G12S, G13D, and rare mutations were also confirmed. We conducted a detailed analysis of the co-occurring KRAS subtypes in conjunction with APC mutations. The most frequently detected co-positive rate with APC was observed in patients carrying the G12D mutation (12.77%), followed by G12V/APC (5.32%) and G13D/APC (5.32%) (Figure 2D). In line with previous studies, patients with G12D showed a higher frequency of mutations than the other KRAS mutant subtypes (31,32).

Figure 2 Co-occurring rate of KRAS and other key regulated genes in CRC. (A-D) The frequency of KRAS, BRAF, APC, and PIK3CA mutation was in 94 samples. (D) The mutational frequency of the combination between different KRAS mutant subtypes and APC was determined in this study. CRC, colorectal cancer.

Impact of driver genes co-mutations on TMB

The correlation between genomic alternations and TMB expression was explored by analyzing individual mutated genes and mutation combinations. Co-occurring mutations of key genes were categorized into 5 subgroups, as follows: KRAS/PIK3CA, KRAS/BRAF, KRAS/APC, KRAS/TP53, and BRAF/APC. The highest expression level of TMB was observed in cases where the KRAS/BRAF mutation occurred (median, 9.54). This was followed by cases with BRAF/APC co-mutation (median, 7.24), and KRAS mutation (median, 6.25) and BRAF mutation (median, 5.26) (Figure 3). Notably, TMB exhibited a considerably higher level in patients harboring co-mutations of KRAS/BRAF, as opposed to patients with KRAS mutation alone (P<0.001). The APC mutation showed similar characteristics when combined with either KRAS or BRAF mutation (KRAS/APC; median, 5.26), demonstrating a significantly higher TMB compared to cases with a single APC mutation (median, 3.95) (KRAS/APC, P=0.003; BRAF/APC, P=0.003).

Figure 3 Evaluation of TMB driven by oncogenes. The impact of KRAS, PIK3CA, BRAF, APC, and TP53 mutations and co-mutations (KRAS/BRAF, KRAS/APC, KRAS/TP53, and BRAF/APC) on TMB. Values are presented as mean ± standard error mean. *, P<0.05; **, P<0.01; ***, P<0.001. (+) represented single gene mutation presence; (−) represented mutation absence; (±) represented mutations possibly presence. TMB, tumor mutational burden.

KRAS subtypes increased TMB driven by APC mutation

In order to examine the impact of KRAS subtypes on the TMB of APC mutation, an analysis was conducted on several significant KRAS subtypes, including G12D, G12C, and G12V, as well as rare subtypes. We found that when KRAS and APC mutations were present in combination, the samples exhibited a higher TMB compared to samples with only the APC mutation (Figure 4A). The median TMB was most significantly impacted by the combination of APC and KRAS mutant subtypes, with the highest effect observed in rare mutation group (median, 5.92), followed by G12V (median, 5.26), G12C (median, 4.61), and G12D (median, 4.61). Our results demonstrated that the TMB of patients with G12V/APC co-mutations was significantly higher compared to those with only APC mutation (P=0.01).

Figure 4 TMB affected by KRAS mutant subtypes. (A) TMB driven by KRAS and APC co-mutations. (B) TMB was affected by KRAS-specific mutant subtypes. *, P<0.05; **, P<0.01. TMB, tumor mutational burden.

To determine the prerequisite for the impact of KRAS co-mutations on TMB, an analysis was conducted on the effects of common KRAS subtypes on TMB (Figure 4B). Further findings emerged from our study, indicating that KRAS G13D presented the highest TMB (median, 6.58), followed by G12V (median, 5.26), G12D (median, 4.61), G12C (median, 3.62), and G12A (median, 3.95), respectively. Notably, TMB levels were significantly higher in patients with KRAS G12V mutation than those with G12D mutation (P=0.009). The expression of TMB in G12V was the highest, whereas G12D presented the lowest TMB both when considered individually or in combination with APC mutation. In contrast to the previous findings regarding the higher TMB observed in lung adenocarcinoma associated with KRAS subtypes and TP53 mutation (19), our study was unable to establish a similar relationship between TMB and the combination of KRAS subtypes and TP53 in CRC (Figure S1). TP53 may potentially reduce the TMB of KRAS G12V and G12C, whereas the combination between G12D and TP53 mutation has been observed to increase the TMB.

Discussion

The current study found that the majority of patients were men (58.25%), and the occurrence of CRC was more common in individuals aged over 50 years (84.46%). In addition, family history of CRC was observed in 46 patients, accounting for 44.66% of the total samples. As mentioned in the literature review, CRC incidence and mortality in the early prenatal to adolescent periods of life progressively increase each year, influenced by age, gender, and physical inactivity (33,34).

In this study, a targeted NGS panel was employed to conduct comprehensive genomic profiling on CRC samples. We determined a large number of candidate mutant genes such as APC, TP53, KRAS, SYNE1, LRP1B, PIK3CA, FBXW7, and ATM, which exhibited a higher frequency of mutations. Among the mutations observed, the genes KRAS, APC, and TP53 were selected for further analysis due to their high frequency of occurrence. It is estimated that approximately 80% of sporadic colorectal tumors and almost all cases of familial adenomatous polyposis (FAP) have APC gene mutations. The APC mutation plays a significant role in the development of adenomas in the intestinal mucosal epithelium, leading to CRC in individuals who carry this high penetrance mutation (5). TP53 is a stress-inducible transcription factor, the mutation of which disrupts the normal function of p53, resulting in the dysregulation of downstream genes involved in cell cycle control, apoptosis, and DNA repair. Notably, the TP53 mutation is observed in 50% of sporadic cases of CRC (35). Totally, mutations in these genes result in the disruption of normal cell cycle regulation and DNA repair mechanisms in CRC cells, leading to uncontrolled tumor growth and progression. Importantly, genomic biomarker testing such as RAS family, BRAF and mismatch repair (MMR) are recommended for testing by relevant guidelines and laboratories for the treatment of CRC population (36,37). KRAS mutation is a highly significant biomarker that has been reported in approximately 20% of NSCLC patients and 45% of CRC patients (38,39). KRAS reportedly modulates the immune response, in cases of CRC and pancreatic cancer in particular. Studies have shown that patients with KRAS, NRAS, or BRAF mutations may exhibit varied responses to treatments (40-42). In addition to the identification of molecular biomarkers, there are also low-incidence markers such as MSI and TMB that can provide guidance for making therapeutic decisions in CRC patients. Previous studies have investigated an association between the mutational analysis of driver genes, including KRAS, APC, TP53, and BRAF as well as their respective mutant subtypes, with TMB levels. TMB is an influential independent predictive factor that refers to the cumulative number of mutations detected in the DNA of a tumor cell. Higher TMB is accompanied by the production of neoantigens, which are helpful for tumor recognition and enhancing the effectiveness of ICIs. The TMB level holds promise as a potential biomarker for predicting the response to ICIs in various cancer types (43-45).

Although the combination of KRAS inhibitors with ICIs or other immunotherapies presents potential therapeutic opportunities for NSCLC, there remains a significant need to address other KRAS mutant subtypes. Different KRAS mutant subtypes can have varying effects on TMB and subsequent treatment outcomes of various cancers. For example, the G12C mutation plays a crucial role in increasing TMB, thereby providing a pivotal advantage in utilizing immunotherapeutic strategies that target neoantigens. The KRAS G12D mutation has been reported to be associated with a lower TMB, resulting in limited response to ICIs in cases of lung adenocarcinoma (19). Consistent with previous studies, our study also found that KRAS G12D, G12C and G12V variants were the most prevalent in CRC population. Although several studies have emphasized the potential of TMB as a predictive biomarker for ICIs treatment, the specific impact of different KRAS mutant subtypes on the efficacy of ICIs remains unclear (46,47). Our study demonstrated that co-mutation of KRAS G12V and APC contributed to a greater increase in TMB compared to single APC mutation in CRC. It was strongly implied that patients with G12V/APC might have a more favorable response to ICIs, whereas those with G12D/APC mutations may not derive significant benefits from ICIs. Our data indicated that the potential advantages of ICIs immunotherapy were observed in CRC patients with specific KRAS subtypes and co-mutation of the APC gene. Other studies have provided corroboration for a decreased prevalence of TMB-H attributed to KRAS subtypes in mCRC as opposed to NSCLC. Nevertheless, there is currently no established standard for the threshold of TMB in various cancers (48,49). However, mutations in KRAS subtypes, including G12V and G12D, continue to serve as significant independent prognostic indicators in individuals with CRC. The KRAS G12V mutation specifically triggers the KRAS signaling pathway, thereby fostering tumorigenesis (50). The study reveals significant differences in median survival rates among patients with KRAS G12V and G12D mutations. Specifically, individuals with the G12V mutation exhibits a notably lower median survival of 31.7 months, whereas those with the G12D mutation display a comparatively higher median survival of 49.2 months (51). Mutations in KRAS G12V have been shown to impact the effectiveness of anti-EGFR therapy and contribute to poor survival rate (52). Furthermore, T-cell receptor (TCR)-recognized KRAS-G12V neoantigens have been identified as a promising therapeutic approach for tumor immunotherapy (53). Hence, these findings underscore the potential prognostic variability associated with the KRAS G12D/G12V alternation, thereby suggesting implications for the design and implementation of future clinical investigations.

Meanwhile, we revealed that the combination of BRAF mutations, along with a single APC mutation, significantly elevated the TMB. Indeed, the BRAF mutation and TMB are currently generally accepted as independent predictive factors for recurrence-free survival (54). The majority of BRAF mutation in this study was the V600E subtype (36.36%, n=4), and considering the limited screening of other mutant subtypes, analyzing the impact of a single BRAF subtype on TMB may present some challenges. Importantly, some co-mutations of KRAS and TP53 exhibited a noticeable reduction in TMB compared to case with only KRAS mutation, such as G12V and G12C. A possible explanation for this difference is that TP53 mutations can modulate the impact of KRAS mutations on tumor progression and response to therapy. It is hypothesized that TP53 mutations, in combination with specific KRAS subtypes, further impair DNA repair mechanisms and increase genomic stability, leading to a decrease in the accumulation of mutations and subsequently reducing TMB levels (55). The combination between G12D and TP53 presented an opposite trend compared to the single G12D mutation. Furthermore, the presence of KRAS mutations across various subtypes did not yield a significant impact on TMB expression of TP53. It might be influenced by various independent factors, including the type of cancer and the individual condition. Therefore, further validation of the correlation between KRAS and TP53 is essential in large cohorts of patients diagnosed with CRC.

It is important to note that the relationship between KRAS mutant subtypes and ICIs is still an area of active research, and more studies are necessary to fully understand the complexities of this interaction. The response to ICIs is influenced by several additional factors, including the tumor microenvironment, overall tumor burden, and the presence of other genetic alterations. Further exploration of the molecular mechanism of immunosuppression in the population with KRAS, BRAF, and APC co-mutation is warranted and will provide an efficient strategy toward achieving the clinical benefits of ICIs.

Conclusions

Our study provides evidence to support the potential use of KRAS as promising therapeutic targets and a key predictive biomarker for immunotherapy ICIs in patients with CRC. Since TMB is upregulated in cases with KRAS G12V/APC co-mutation, there could be an enhanced response to ICIs. Our data strongly suggest the potential benefits of immunotherapy with ICIs in CRC patients with specific KRAS subtypes and co-mutation groups. However, further validation in larger clinical research datasets is necessary to confirm these findings.

Acknowledgments

We thank the editors of this journal for their patience and valuable feedback. We acknowledge the significant contributions of prior researchers in this field.

Funding: This research was supported by Application Study of Hemi-M Methylation Sequencing for Benign and Malignant Diagnosis in Lung Nodules (grant No. KQJSCX20180330124428928), Research Project on the Industrial Application of Basic Research on Synthetic Biological Devices to Intervene in Bladder Cancer (grant No. CJGJZD20200617102403009), and Technological Development for Methylation Hemi-WGBS of Precise Molecular Diagnosis (grant No. JSGG20180508152646606).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-600/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-600/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-600/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-600/coif). X.W., X.Z., M.X., Zhi Zheng, Y.Z. report that they are employees of HaploX Biotechnology, Co., Ltd. The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by Sun Yat-sen University Cancer Center Ethics Committee (B2019-031-01, Guangdong, China) and informed consent was provided by all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Rawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Prz Gastroenterol 2019;14:89-103. [Crossref] [PubMed]
2. Islami F, Goding Sauer A, Miller KD, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin 2018;68:31-54. [Crossref] [PubMed]
3. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
4. Dekker E, Tanis PJ, Vleugels JLA, et al. Colorectal cancer. Lancet 2019;394:1467-80. [Crossref] [PubMed]
5. Armaghany T, Wilson JD, Chu Q, et al. Genetic alterations in colorectal cancer. Gastrointest Cancer Res 2012;5:19-27. [PubMed]
6. Li L, Hu Y, Xu Y, et al. Mathematical modeling the order of driver gene mutations in colorectal cancer. PLoS Comput Biol 2023;19:e1011225. [Crossref] [PubMed]
7. Hankey W, Frankel WL, Groden J. Functions of the APC tumor suppressor protein dependent and independent of canonical WNT signaling: implications for therapeutic targeting. Cancer Metastasis Rev 2018;37:159-72. [Crossref] [PubMed]
8. Tortola S, Marcuello E, González I, et al. p53 and K-ras gene mutations correlate with tumor aggressiveness but are not of routine prognostic value in colorectal cancer. J Clin Oncol 1999;17:1375-81. [Crossref] [PubMed]
9. De Roock W, Claes B, Bernasconi D, et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 2010;11:753-62. [Crossref] [PubMed]
10. Huang D, Sun W, Zhou Y, et al. Mutations of key driver genes in colorectal cancer progression and metastasis. Cancer Metastasis Rev 2018;37:173-87. [Crossref] [PubMed]
11. Zhuang Y, Wang H, Jiang D, et al. Multi gene mutation signatures in colorectal cancer patients: predict for the diagnosis, pathological classification, staging and prognosis. BMC Cancer 2021;21:380. [Crossref] [PubMed]
12. Zhu G, Pei L, Xia H, et al. Role of oncogenic KRAS in the prognosis, diagnosis and treatment of colorectal cancer. Mol Cancer 2021;20:143. [Crossref] [PubMed]
13. Marks JL, Broderick S, Zhou Q, et al. Prognostic and therapeutic implications of EGFR and KRAS mutations in resected lung adenocarcinoma. J Thorac Oncol 2008;3:111-6. [Crossref] [PubMed]
14. Haas M, Ormanns S, Baechmann S, et al. Extended RAS analysis and correlation with overall survival in advanced pancreatic cancer. Br J Cancer 2017;116:1462-9. [Crossref] [PubMed]
15. Stewart CJ, Amanuel B, Grieu F, et al. KRAS mutation and microsatellite instability in endometrial adenocarcinomas showing MELF-type myometrial invasion. J Clin Pathol 2010;63:604-8. [Crossref] [PubMed]
16. Lee JK, Sivakumar S, Schrock AB, et al. Comprehensive pan-cancer genomic landscape of KRAS altered cancers and real-world outcomes in solid tumors. NPJ Precis Oncol 2022;6:91. [Crossref] [PubMed]
17. Wang Q, Tang Z, Li C, et al. Evaluating distinct KRAS subtypes as potential biomarkers for immune checkpoint inhibitor efficacy in lung adenocarcinoma. Front Immunol 2023;14:1297588. [Crossref] [PubMed]
18. Judd J, Abdel Karim N, Khan H, et al. Characterization of KRAS Mutation Subtypes in Non-small Cell Lung Cancer. Mol Cancer Ther 2021;20:2577-84. [Crossref] [PubMed]
19. Gao G, Liao W, Ma Q, et al. KRAS G12D mutation predicts lower TMB and drives immune suppression in lung adenocarcinoma. Lung Cancer 2020;149:41-5. [Crossref] [PubMed]
20. Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017;357:409-13. [Crossref] [PubMed]
21. Cristescu R, Mogg R, Ayers M, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 2018;362:eaar3593. [Crossref] [PubMed]
22. Zhang M, Yang H, Chen L, et al. Pathological complete response in MMR-deficient/MSI-high and KRAS-mutant patient with locally advanced rectal cancer after neoadjuvant chemoradiation with immunotherapy: A case report. Front Oncol 2022;12:926480. [Crossref] [PubMed]
23. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228-47. [Crossref] [PubMed]
24. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010;26:589-95. [Crossref] [PubMed]
25. Bamford S, Dawson E, Forbes S, et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer 2004;91:355-8. [Crossref] [PubMed]
26. Gandara DR, Paul SM, Kowanetz M, et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat Med 2018;24:1441-8. [Crossref] [PubMed]
27. Budczies J, Allgäuer M, Litchfield K, et al. Optimizing panel-based tumor mutational burden (TMB) measurement. Ann Oncol 2019;30:1496-506. [Crossref] [PubMed]
28. Wu HX, Wang ZX, Zhao Q, et al. Designing gene panels for tumor mutational burden estimation: the need to shift from 'correlation' to 'accuracy'. J Immunother Cancer 2019;7:206. [Crossref] [PubMed]
29. Setaffy L, Langner C. Microsatellite instability in colorectal cancer: clinicopathological significance. Pol J Pathol 2015;66:203-18. [Crossref] [PubMed]
30. Umar A, Boland CR, Terdiman JP, et al. Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J Natl Cancer Inst 2004;96:261-8. [Crossref] [PubMed]
31. Jun E, Koo B, Kim EJ, et al. Analysis of KRAS Mutation Subtype in Tissue DNA and Cell-Free DNA Using Droplet Digital PCR and the Function of Cell-Free DNA as a Recurrence Predictive Marker in Pancreatic Cancer. Biomedicines 2021;9:1599. [Crossref] [PubMed]
32. Mazurenko NN, Gagarin IM, Tsyganova IV, et al. The frequency and spectrum of KRAS mutations in metastatic colorectal cancer. Vopr Onkol 2013;59:751-5. [PubMed]
33. Akimoto N, Ugai T, Zhong R, et al. Rising incidence of early-onset colorectal cancer - a call to action. Nat Rev Clin Oncol 2021;18:230-43. [Crossref] [PubMed]
34. Li N, Lu B, Luo C, et al. Incidence, mortality, survival, risk factor and screening of colorectal cancer: A comparison among China, Europe, and northern America. Cancer Lett 2021;522:255-68. [Crossref] [PubMed]
35. Takayama T, Miyanishi K, Hayashi T, et al. Colorectal cancer: genetics of development and metastasis. J Gastroenterol 2006;41:185-92. [Crossref] [PubMed]
36. Cicenas J, Tamosaitis L, Kvederaviciute K, et al. KRAS, NRAS and BRAF mutations in colorectal cancer and melanoma. Med Oncol 2017;34:26. [Crossref] [PubMed]
37. Sanchez-Ibarra HE, Jiang X, Gallegos-Gonzalez EY, et al. KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study. PLoS One 2020;15:e0235490. [Crossref] [PubMed]
38. Pinto R, Petriella D, Lacalamita R, et al. KRAS-Driven Lung Adenocarcinoma and B Cell Infiltration: Novel Insights for Immunotherapy. Cancers (Basel) 2019;11:1145. [Crossref] [PubMed]
39. Bos JL, Fearon ER, Hamilton SR, et al. Prevalence of ras gene mutations in human colorectal cancers. Nature 1987;327:293-7. [Crossref] [PubMed]
40. Tran E, Robbins PF, Lu YC, et al. T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer. N Engl J Med 2016;375:2255-62. [Crossref] [PubMed]
41. Neumann F, Wagner C, Kubuschok B, et al. Identification of an antigenic peptide derived from the cancer-testis antigen NY-ESO-1 binding to a broad range of HLA-DR subtypes. Cancer Immunol Immunother 2004;53:589-99. [Crossref] [PubMed]
42. Chen YT, Scanlan MJ, Sahin U, et al. A testicular antigen aberrantly expressed in human cancers detected by autologous antibody screening. Proc Natl Acad Sci U S A 1997;94:1914-8. [Crossref] [PubMed]
43. Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2014;371:2189-99. [Crossref] [PubMed]
44. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N Engl J Med 2017;377:2500-1. [Crossref] [PubMed]
45. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015;348:124-8. [Crossref] [PubMed]
46. Sha D, Jin Z, Budczies J, et al. Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors. Cancer Discov 2020;10:1808-25. [Crossref] [PubMed]
47. Dong ZY, Zhong WZ, Zhang XC, et al. Potential Predictive Value of TP53 and KRAS Mutation Status for Response to PD-1 Blockade Immunotherapy in Lung Adenocarcinoma. Clin Cancer Res 2017;23:3012-24. [Crossref] [PubMed]
48. Schrock AB, Ouyang C, Sandhu J, et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann Oncol 2019;30:1096-103. [Crossref] [PubMed]
49. Innocenti F, Ou FS, Qu X, et al. Mutational Analysis of Patients With Colorectal Cancer in CALGB/SWOG 80405 Identifies New Roles of Microsatellite Instability and Tumor Mutational Burden for Patient Outcome. J Clin Oncol 2019;37:1217-27. [Crossref] [PubMed]
50. Bai B, Shan L, Xie B, et al. Mutations in KRAS codon 12 predict poor survival in Chinese patients with metastatic colorectal cancer. Oncol Lett 2018;15:3161-6. [PubMed]
51. Olthof PB, Buettner S, Andreatos N, et al. KRAS alterations in colorectal liver metastases: shifting to exon, codon, and point mutations. Br J Surg 2022;109:804-7. [Crossref] [PubMed]
52. Wang Y, Lei F, Rong W, et al. Positive feedback between oncogenic KRAS and HIF-1α confers drug resistance in colorectal cancer. Onco Targets Ther 2015;8:1229-37. [PubMed]
53. Lu D, Chen Y, Jiang M, et al. KRAS G12V neoantigen specific T cell receptor for adoptive T cell therapy against tumors. Nat Commun 2023;14:6389. [Crossref] [PubMed]
54. Eckardt J, Schroeder C, Martus P, et al. TMB and BRAF mutation status are independent predictive factors in high-risk melanoma patients with adjuvant anti-PD-1 therapy. J Cancer Res Clin Oncol 2023;149:833-40. [Crossref] [PubMed]
55. Peng J, Fang S, Li M, et al. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis. Open Life Sci 2023;18:20220652. [Crossref] [PubMed]